Who is this presentation for?

Those using network visualization and network analytics to develop theories and solve problems

Prerequisite knowledge

A basic understanding of network analytics and the Jupyter Notebook

What you'll learn

Explore a simple, flexible architecture that can help create beautiful JavaScript networks without ditching the Jupyter Notebook

Description

The potential uses of network analytics and visualizations are extensive, with applications ranging from social network analysis to environmental science to better understanding how political revolutions spread. However, many of the tools most commonly used for these types of analysis, particularly Python modules like NetworkX, are not designed to produce aesthetically pleasing, interactive visualizations that support the development of theories and inferences. Much of the time, data scientists using tools like Jupyter are left trying to work with network visualizations that look like hairballs or spending a great deal of time trying to use unfamiliar tools like JavaScript or Gephi.

Daina Bouquin and John DeBlase share a simple, flexible architecture that can help create beautiful JavaScript networks without ditching the Jupyter Notebook—a side-by-side Jupyter network visualization GUI that allows users to quickly create beautiful visualizations that can help drive research processes. Daina and John then offer a demonstration that illustrates how a librarian can take advantage of this infrastructure to better understand authorship and publishing tendencies in her field.

Daina Bouquin

Harvard-Smithsonian Center for Astrophysics

Daina Bouquin is the head librarian of the Harvard-Smithsonian Center for Astrophysics in Cambridge, MA. Her work aims to lower social and technical barriers that impact the astronomy community’s ability to create and share new knowledge. Her research interests focus primarily on how libraries can support open science, research software preservation, emerging computational methods, and the history of science. Daina is currently working toward an MS in data analytics at CUNY’s School of Professional Studies.

John D

CUNY Building Performance Lab

John DeBlase is lead developer for the CUNY Building Performance Lab, where he helps develop Python-based statistical modeling applications for city-wide energy management research. A developer, data scientist, and musician from Queens, NY, John’s personal research revolves around the development musical intelligence systems using natural language processing techniques with a focus on real-time human-computer interaction. John is interested in developing applications for data scientists that emphasize interactive data visualization, leveraging the best tools currently available in both Python and Node.js.